1. Field of Invention
This invention relates to the field of real time optimization and control using Model Predictive Controller. More particularly, the present invention relates to integration of model predictive controllers for a large process unit, its sub-processes including regulatory controllers for robust performance.
2. Description of Prior Art
Model Predictive Control has been applied to a wide range of processes, to a simple unit operation to plant unit operation involving multiple unit operations. However, quality of performance of Model Predictive Controller (MPC) as practiced in the art varies considerably with the size of its application; tuning and design can take considerable engineering effort.
As practiced in the art, tuning of a MPC is a trial and error endeavor that works well under a narrow and defined range of operation, depending on the severity of disturbances and the degree of model uncertainty present, the controller tuning requires frequent updating. In other words, performance of MPC lacks robustness for sustained improved profitability of process operation under changing conditions. This problem is exacerbated as the size of application increases.
Tuning of a MPC is primarily oriented towards individual variable based tuning in isolation of other variables. This approach does not lend itself towards a simple and unifying way of controlling stability of the process under control. The piecemeal approach to setting tuning of the controller makes the controller susceptible to wide range of performance degradation depending on the extent of disturbance effects as well as model mismatch effects. Thus, tuning developed under one set of conditions are often not appropriate for different set of conditions. Consequently, performance of a MPC can be erratic and in the worst case, requiring continual tuning changes.
In the prior art, attempts have been made to link multiple MPCs for a large process unit albeit with limited success. One such approach used in practice is what is generally known as “Composite LP” method in which a number of MPCs are linked via an overall LP for steady state optimization whilst individual MPCs perform their respective dynamic move actions. This approach requires extensive engineering efforts to build and maintain such a system. Another approach that has been mentioned in practice relate to U.S. Pat. No. 6,122,555. In this approach, an attempt has been made to link a number of MPCs for steady state optimization but with addition of seeking an amalgamated solution of dynamic move calculation and steady state optimization. Both these two approaches have been tried to solve the problem of linking multiple MPCs by addition of refinements of optimization on top of basic MPC lacking its own robustness.
Although both these above-mentioned approaches offer a solution to the underlying problem of building and maintaining large-scale model predictive controller(s), however, they do not tackle the problem fully and satisfactorily. Both these approaches attempt to offer solution to large-scale integration without any changes either to the basic structure of Model Predictive Control or the methodology of its application. Both of these approaches are built upon a basic structure of Model Predictive Control that has its own shortcomings and weaknesses.
For most part, in practice most efforts have been spent in trying to improve the robustness of performance by improving the accuracy of the models used in the controller. In practice, not all process instability can be attributed solely to modeling error as often bad tuning also induces closed loop instability. Recently, in patent U.S. Pat. No. 6,381,505 an attempt has been made to account explicitly model uncertainty in steady state target calculation for improved robustness of the controller performance. This method solves the problem of cycling in the steady state controller due to modeling error. However, apparently, it would not help when there are no modeling errors and cycling is due to bad controller tuning.
What is needed in the art is a more robust base level Model Predictive Control that can be intergraded as a part of a large-scale application whilst maintaining its own performance. In addition what is needed in the art is a simple and powerful method of building and maintaining be it small size or large size model predictive controller be it on a small unit operation or on a large integrated process operations as a whole that would consistently deliver robust performance.